Factors Impacting Intentions in Adopting Artificial Intelligence-Based Solutions in Agriculture: An Indian Context

Abstract

Earth is now a habitat of eight billion human beings who depend on the limited resources available on the planet to survive. The increasing population is constantly exerting pressure on present agricultural production systems and demands for increased production to ensure food security across the globe. Digital technologies including Artificial Intelligence (AI) enable the farmers and facilitators for making better decisions during crop lifecycle management which in turn leads to lesser damages and increased productivity. Through the use of machine learning algorithms, AI systems can analyze vast amounts of data, including weather patterns, soil conditions, and crop characteristics, to provide valuable insights for farmers. This enables optimized resource allocation, precise irrigation and fertilization techniques, and timely pest detection and control, ultimately increasing crop yields while reducing costs and environmental impact. Despite the large number of perceived benefits and government plans, the adoption level of AI based solutions in agriculture is quite low. As a step towards bridging the gap between the present situation of agricultural production and a target of zero hunger identified as sustainable development goal (SDG) by the United Nations, this study empirically evaluates the determinants that influence adoption of AI-based solutions in agriculture. To understand and evaluate the perspectives of farmers (end-users of the solution) and facilitators (enablers in the agricultural system) involved in the diffusion of new agricultural technologies, this study uses an integrated framework built on three eminent theories from Information Systems, namely Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovation (DoI) and Technology-Organization-Environment Framework (TOE Framework). Using survey data of farmers and facilitators from two states in Northern India, this study examines the interaction of independent variables and validates the proposed framework using Structural Equation Model (SEM). The analysis of farmers' data reveals that user expectations, technology factors, social influence and facilitating conditions are significant factors influencing their intention to adopt AI-based solutions. Farmers recognize the benefits of AI technology in terms of increased productivity, efficiency, and ease of use. Compatibility with existing practices and the availability of resources and information are also important considerations. In the case of facilitators, the study highlights that facilitating conditions, such as knowledge, resources, and support, plays a crucial role in their intention to adopt AI-based solutions. vii Compatibility is also a significant factor influencing adoption intent of facilitators. Facilitators express concerns about the cost, efficiency, and technical risks associated with the new technology. The given framework significantly explains the adoption intention of farmers and facilitators, hence enhances understanding of researchers and practitioners to increase adoption of AI-based solutions for sustainable agriculture. This study contributes to the understanding of the factors influencing the adoption of AI-based solutions in agriculture and provides practical implications for promoting their adoption. By addressing the specific needs and challenges faced by farmers and facilitators, the study paves the way for the successful integration of AI technology in agriculture, leading to improved productivity, sustainability, and decision-making in the sector.